Accommodating geometric and thermodynamic variability for forward-looking infrared sensors

Our work has focused on deformable template representations of geometric variability in automatic target recognition (ATR). Within this framework we have proposed the generation of conditional mean estimates of pose of ground-based targets remotely sensed via forward-looking infrared radar (FLIR) system. Using the rotation group parameterization of the orientation space and a Bayesian estimation framework. conditional mean estimators are defined ori tho rotation group with minimum mean squared error (MMSE) performance hounds calculated following [1]. This paper focuses an the accommodation of thermodynamic variation. Our new approach relaxes assumptions of the target's underlying thermodynamic state, expanding thermodynamic state as a scalar field. Estimation within the deformable template setting poses geometric and thermodynamic variation as a joint inference. MMSE post estimators for geometric variation are derived, demonstrating the ''cost'' of accommodating thermodynamic variability. Performance is quantitatively examined, and simulations are presented.